MétaCan
Menu
Back to cohort
Record W3092393873 · doi:10.1017/s0144686x20001348

Between loneliness and belonging: narratives of social isolation among immigrant older adults in Canada

2020· article· en· W3092393873 on OpenAlexaffabout
Sharon Koehn, Ilyan Ferrer, Shari Brotman

Bibliographic record

VenueAgeing and Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcGill UniversityUniversity of CalgarySimon Fraser University
Fundersnot available
KeywordsLonelinessSocial isolationImmigrationIsolation (microbiology)Agency (philosophy)NarrativeSociologyResistance (ecology)PsychologyGender studiesRefugeeSocial psychologyDevelopmental psychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Abstract Research points to a higher risk for social isolation and loneliness among new immigrant and refugee older adults. Our article draws from a research project that explored the everyday stories of ageing among 19 diverse immigrant older adults in Canada. To capture their experiences of loneliness and social isolation, we use four illustrative cases derived from a structural approach to life-story narrative. To these we apply the intersectional lifecourse analytical lens to examine how life events, timing and structural forces shape our participants’ experiences of social isolation and loneliness. We further explore the global and linked lives of our participants as well as the categories of difference that influence their experiences along the continua of loneliness to belonging, isolation to connection. Finally, we discuss how an understanding of sources of domination and expressions of agency and resistance to these forces might lead us to solutions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.267
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations50
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueAgeing and SocietySame topicHealth disparities and outcomesFrench-language works237,207